SIMUnet: Leveraging open-source machine learning to explore the interplay between parton distribution functions and potential new physics

PBSP (Physics Beyond the Standard Proton) is an ERC funded project, led by Prof. Maria Ubiali and based at the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge. The projects focuses on the global interpretation of the LHC data in terms of indirect searches for new physics, by providing a robust framework to globally interpret all subtle deviations from the SM predictions that might arise at colliders.

The PBSP team has developed the SIMUnet methodology, which uses machine learning techniques to study the interplay between PDFs and potential new physics signals. Drawing upon the NNPDF methodology, SIMUnet provides an augmented framework with a suite of tools that allows the user to

• Perform simultaneous fits of PDFs and EFT coefficients
• Perform Fixed-PDF fits of EFT coefficients
• Assess the possible absorption of new physics by the PDFs
• Study the interplay between PDFs and EFT coefficients
• Analyse the results and produce posterior distributions, correlations, confidence levels, and general quality metrics and plots

Documentation

The documentation is available at the official SIMUnet website.

Install

See the SIMUnet installation tutorial.

Cite

The SIMUnet code has been developed in the original paper

@article{Iranipour:2022iak,
    author = "Iranipour, Shayan and Ubiali, Maria",
    title = "{A new generation of simultaneous fits to LHC data using deep learning}",
    eprint = "2201.07240",
    archivePrefix = "arXiv",
    primaryClass = "hep-ph",
    doi = "10.1007/JHEP05(2022)032",
    journal = "JHEP",
    volume = "05",
    pages = "032",
    year = "2022"
}

and the official release

@article{Costantini:2024xae,
    author = "Costantini, Mark N. and Hammou, Elie and Kassabov, Zahari and Madigan, Maeve and Mantani, Luca and Morales Alvarado, Manuel and Moore, James M. and Ubiali, Maria",
    title = "{SIMUnet: an open-source tool for simultaneous global fits of EFT Wilson coefficients and PDFs}",
    eprint = "2402.03308",
    archivePrefix = "arXiv",
    primaryClass = "hep-ph",
    month = "2",
    year = "2024"
}

so please cite these references if you use the code.

Bugs and contributions

If you find a bug or have a new feature idea, do not hesitate to drop them in our issue tracker.